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Modellierung der thermischen Zersetzung bei Reibung von Verbundwerkstoffen auf der Basis von garnverstärkten Polymermatrizen mit künstlichen neuronalen Netzen
Author(s) -
Kopal I.,
Vršková J.,
Ondrušová D.,
Harničárová M.,
Valíček J.,
Koleničová Z.
Publication year - 2019
Publication title -
materialwissenschaft und werkstofftechnik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.285
H-Index - 38
eISSN - 1521-4052
pISSN - 0933-5137
DOI - 10.1002/mawe.201800178
Subject(s) - materials science , thermogravimetric analysis , composite number , composite material , thermal stability , thermal decomposition , artificial neural network , isothermal process , thermogravimetry , computer science , engineering , chemical engineering , thermodynamics , chemistry , physics , organic chemistry , machine learning
The presented work deals with the application of artificial neural networks in the modelling of the thermal decomposition process of friction composite systems based on polymer matrices reinforced by yarns. The thermal decomposition of the automotive clutch friction composite system consisting of a polymer blend reinforced by yarns from organic, inorganic and metallic fibres impregnated with resin, as well as its individual components, was monitored by a method of non‐isothermal thermogravimetry over a wide temperature range. A supervised feed‐forward back‐propagation multi‐layer artificial neural network model, with temperature as the only input parameter, has been developed to predict the thermogravimetric curves of weight loss and time derivative of weight loss of studied friction composite system and its individual components acquired at a fixed constant heating rate under a pure dry nitrogen atmosphere at a constant flow rate. It has been proven that an optimized model with a 1‐25‐6 architecture of an artificial neural network trained by a Levenberg‐Marquardt algorithm is able to predict simultaneously all the analyzed experimental thermogravimetric curves with a high level of reliability and that it thus represents the highly effective artificial intelligence tool for the modelling of thermal stability also of relatively complicated friction composite systems.